Best Coffee Shop in Surry Hills¶

Use Case:¶

Find the best coffee shops in and around 2 Holt Street Surry Hills

Technology Stack:¶

Big Data Engineering
Data Science
Advanced Analytics
Geo-spatial calulations
Data Visualization

Solution Architecture¶

Data Collection:
- --> https://freeonlinesurveys.com/

Infra-structure:
--> Docker
Programming languges user:
--> R
--> Python
Presentation Layer / Development Env:
--> Jupyter Notebook

How do we collect data?¶

We (DnA team) go for coffee everyday in the morning to the cafe short listed. We take photo snap at the cafe. After reaching office, we build a survey question per iteration and circulate to the people who had coffee at that day. The data is captured in https://freeonlinesurveys.com/ and we get it when we do data analytics.

Out[5]:

Who are we ?¶

Coffee Shops that we visited¶

In [2]:

import matplotlib.image as mpimg
plt.figure(figsize=(100,100))
img=mpimg.imread('data/maps_coffeeshops.png')

imgplot = plt.imshow(img)

Out[57]:

Parameter analysisd¶

	Iteration_1	Iteration_2	Iteration_3
0	Taste_Quantity	Taste_Quantity	Taste_consistency
1	Price	Price	service_speed_consistency
2	Service_Speed	Service_Speed	food_taste_consistency
3	Atmosphere_Friendliness	Eating_option	Free_coffee
4	Walking_Distance	Loyalty_cards	customer_service_consistency
5		hospitality
6		Place_4_Hangout

Iteration 1 - Results¶

In [182]:

df_plot.sort_values("Score")\
       .iloc[:,:6]\
       .plot(x='shop_address',kind='bar', stacked=True, figsize=(12,6), rot=65)
plt

Out[182]:

<module 'matplotlib.pyplot' from '/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py'>

In [183]:

plt.figure(figsize=(20,10))
parallel_coordinates(df_plot[['shop_name', 'Taste_Quantity', 'Price', 'Service_Speed','Atmosphere_Friendliness', 'Walking_Distance','Walking_Distance']],
                     "shop_name"
                     )

plt.show()

Iteration 2 - Results¶

Iteration Final - Results¶